Team Bioinformatics is involved in a wide variety of activities related to gene function analysis, analysis of high-throughput data, modelling of genetic and protein interaction networks, statistical data analysis, and the development of informatics infrastructure.

Genome Editing and Mutagenesis

Newly developed genome editing technologies using transcription activator-like effector nucleases (TALENs) and nucleases of the bacterial CRISPR/Cas9 defense system are becoming increasingly popular as a means to manipulate the mouse genome and to produce mouse models of human disease.

As CRISPR/Cas9 mediated screens with pooled guide libraries in somatic cells become increasingly established, an unmet need for rapid and accurate companion informatics tools has emerged. We have developed a lightweight and efficient software to easily manipulate large raw next generation sequencing datasets derived from such screens into informative relational context with graphical support. The advantages of the software entitled ENCoRE (Easy NGS-to-Gene CRISPR REsults) include a simple graphical workflow, platform independence, local and fast multithreaded processing, data pre-processing and gene mapping with custom library import.

One technology to produce mouse models for human disease is the  microinjection of transcription activator-like effector nucleases (TALENs) and synthetic oligodeoxynucleotides into one-cell embryos. We developed a webportal TALENdesigner for the genomewide prediction and visualization of genomic TAL (transcription activator-like) effector nuclease binding and cleavage sites.

Mutation of genes by insertional mutagenesis and the subsequent analysis of the resultant mutant phenotype is an essential method towards an understanding of gene function. The Bioinformatics team analysed and annotated more than 75.000 gene trap vector insertions into the mouse genome and developed a webportal for the library of mutant embryonic stem cells of the German Gene Trap Consortium (GGTC).

Disease-relevant Protein Interaction Networks

In order to understand human disease it is indispensable to understand the role of disease-related genes in the complex cellular regulatory networks. We analyze protein-interaction data generated by affinity purification and mass spectrometry aiming to understand the complex interactions of disease-related proteins in mammalian cells. Using network methods and functional data we seek to identify clusters of proteins relevant for human diseases. We developed a web-based protein information portal (DiGtoP – From Disease Genes to Protein Pathways)and analyze the data to extract insight into the regulatory cellular networks

Genetic Interaction Networks

We focus on the modelling of genetic interactions and extend regulatory networks by prediction of genetic interactions integrating high-throughput gene expression data and in-silico promoter analyses based on sophisticated statistical analyses. In this context, we built up Mouse IDGenes, a reference database for genetic interactions in the developing mouse brain. The database holds manually curated gene expression data and the genetic interactions at the developing mid-/hindbrain boundary and additional regions of the developing mouse CNS.